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A. Sinha and K. Ramani Multi-Scale Kernels Using Random Walks Computer Graphics Forum 33

Article first published online: 21 NOV 2013 | DOI: 10.1111/cgf.12264

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We introduce novel multi-scale kernels using the random walk framework and derive corresponding embeddings and pairwise distances. The fractional moments of the rate of continuous time random walk (equivalently diffusion rate) are used to discover higher order kernels (or similarities) between pair of points. The formulated kernels are isometry, scale and tessellation invariant, can be made globally or locally shape aware and are insensitive to partial objects and noise based on the moment and influence parameters. In addition, the corresponding kernel distances and embeddings are convergent and efficiently computable.

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